Mobile automation apparatuses are increasingly being used in retail environments to perform pre-assigned tasks, such as inventory tracking. To perform these tasks, some mobile automation apparatuses rely on various sensors to navigate within the retail environment. As the mobile automation apparatus navigates the environment, tight positional constraints should be satisfied for the mobile automation apparatus to capture high quality imaging data. As such, the mobile automation apparatus generally attempts to follow a single, static predefined path that is believed to be optimal for its environment. However, due to the highly varying and dynamic nature of a retail environment, a mobile automation apparatus often deviates from the single predefined path to avoid obstacles in arbitrary locations. When such deviations occur, under a conventional approach, the deviated path cannot use the learned control inputs, nor will the mobile automation apparatus attempt to learn control inputs along the deviation. Instead, the mobile automation apparatus will simply attempt to navigate back to the single predefined optimal path.
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The apparatus and method components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.